AI automation services built into the systems you already run
We find the repetitive, rules-heavy work in your operations and automate it inside your existing systems: documents, payments, work queues and reporting. Everything is measured on your data before it goes live.
- AI document processing live in a claims platform
- Confidence thresholds you control
- Your cloud, your choice of model
Work we take off your team's plate
Each one runs inside the systems your team already uses, with people reviewing what the AI isn't sure about.
Document processing
Correspondence, payment documents and forms turned into validated data.
Intelligent document processingKnowledge assistants
Answers from your policies, contracts and files, with every answer linked to its source.
AI knowledge assistantsConversational analytics
Business questions in plain English, answered from your own data with the query shown.
Conversational analyticsAI agents
Agentic steps inside document workflows, with guardrails.
AI agent developmentPayment matching
Electronic payment files and remittances matched to the right claims automatically.
Allocation and routing
Records matched to claims by confidence, with exceptions sent to a review queue.
Workflow automation
Work queues, notifications, letters and background jobs that used to need a person.
Data and reporting
Finance data platforms and dashboards on Azure and Power BI.
How we automate without losing control
Nothing is promised until it has been measured on your own data.
Find the work worth automating
High volume, clear rules and costly errors come first.
Look for structured data first
If a system can send the data directly, that beats AI every time.
Pilot on your real data
We measure accuracy and cost before promising anything.
Build it into your systems
Confidence thresholds, human review, audit logs and cost tracking are part of the build.
Keep improving
We check results against verified samples, tune and expand.
Where AI fits, and where it doesn't
We'll always tell you when you don't need AI. When one payer switched to electronic payment files, we switched AI off for that payer.
Good fit for AI
- Documents with varied layouts and scan quality
- High volumes that people spend hours reading
- Work with clear rules for what “correct” looks like
Better without AI
- Data that another system can send in a structured file
- Decisions that need judgment and have no review step
- Tiny volumes where setup costs more than the work
Measured before it ships
The engineering behind AI that holds up after the demo.
- Before every release
Accuracy tested on real questions
We test AI features against real questions and score accuracy before every release, a practice our team has run across dozens of production AI applications.
- From the first run
Costs you can see
AI usage is recorded per document from the first run, and our engineers cut running costs by routing each request to the most cost-effective model that meets the quality bar.
- Standard
Private by design
Personal data masking, encryption and strict tenant isolation are standard, with a BAA where health information is involved.
- No lock-in
Your cloud, your model
We build on AWS, Azure or Google Cloud, with OpenAI, Anthropic, Google, Mistral or open-source models, so you are never locked into one provider.
AI running inside a claims platform today
For a workers' compensation pharmacy claims administrator, we built AI document processing into the claims platform we had rebuilt for them. Correspondence from multiple lockboxes becomes claimant, prescription, drug-code and response-code data.
- 212
- real documents (820 pages) piloted before the full build
- Automatic
- allocation above the confidence threshold; people review the rest
AI and automation FAQ
What can AI automate in our business?
Usually the work where people read something and type it somewhere else: documents, payment details, routing decisions with clear rules. We look at your volumes and error costs to find where it pays off first.
Which AI models do you use?
We choose models per task and run them through enterprise services in your cloud, such as Amazon Bedrock. Models sit behind a standard interface, so you can switch later without a rebuild.
How do you stop AI from making things up?
We ground every answer in the source page, validate it against rules and formats, attach a confidence score and send anything uncertain to a person.
Will our data be used to train AI models?
We use enterprise AI services whose terms say customer data isn't used for training, and we confirm those terms with you before we start.
How do we start?
With a short call, then a pilot on a representative sample of your real work, so you see accuracy and cost before committing to a full build. The pilot has a fixed scope and fixed fee, credited toward the full build.
Find the work worth automating
In a 30-minute call with our CEO or CTO, we'll look at where your team spends its time and which of that work AI can safely take over.
- A 30-minute callYour volumes, error costs and data sources
- A pilot on your real workAccuracy and cost measured on a representative sample, for a fixed fee credited toward the build
- A full build, only if it paysBuilt into your systems, with review and cost tracking